%0 Journal Article
%A Dreber, Anna
%A Pfeiffer, Thomas
%A Almenberg, Johan
%A Isaksson, Siri
%A Wilson, Brad
%A Chen, Yiling
%A Nosek, Brian A.
%A Johannesson, Magnus
%T Using prediction markets to estimate the reproducibility of scientific research
%D 2015
%R 10.1073/pnas.1516179112
%J Proceedings of the National Academy of Sciences
%P 15343-15347
%V 112
%N 50
%X There is increasing concern about the reproducibility of scientific research. For example, the costs associated with irreproducible preclinical research alone have recently been estimated at US$28 billion a year in the United States. However, there are currently no mechanisms in place to quickly identify findings that are unlikely to replicate. We show that prediction markets are well suited to bridge this gap. Prediction markets set up to estimate the reproducibility of 44 studies published in prominent psychology journals and replicated in The Reproducibility Project: Psychology predict the outcomes of the replications well and outperform a survey of individual forecasts.Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants’ individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a “statistically significant” finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications.
%U https://www.pnas.org/content/pnas/112/50/15343.full.pdf